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The time series analysis and prediction techniques are highly valued in many application fields, such as economy, medicine and biology, environmental sciences or meteorology, among others. In the last years, there is a growing interest in the sustainable and optimal management of a resource as scarce as essential: the water. Forecasting techniques for water management can be used for different time horizons from the planning of constructions that can respond to long-term needs, to the detection of anomalies in the operation of facilities or the optimization of the operation in the short and medium term. In this paper, a deep neural network is specifically designed to predict water consumption in the short-term. Results are reported using the time series of water consumption for a year and a half measured with 10-min frequency in the city of Murcia, the seventh largest city in Spain by number of inhabitants. The results are compared with K Nearest Neighbors, Random Forest, Extreme Gradient Boosting, Seasonal Autoregressive Integrated Moving Average and two persistence models as naive methods, showing the proposed deep learning model the most accurate results. [ABSTRACT FROM AUTHOR]

Potable water utilities are currently making great efforts to reduce leakage rates and assure long-term supply to the population due to the challenges of climate change, growing population and water shortage scenarios that have been on them over the last years. One of the most employed methods to reduce leakage includes the installation of pressurereducing valves along the water distribution network and the utilization of pressure management schemes. Pressure management includes different types of control models, which are applied according to the requirements of each site. The most advanced and sophisticated scheme is critical point control, which relies on a flow signal from a measuring device or online communication between the critical point and the valve. This paper proposes the utilization of a seasonal autoregressive integrated moving average, or the SARIMA model, to correlate pressure at the outlet of the valve and pressure on the critical point of the area supplied, aiming to set a fixed pressure in the critical point. The SARIMA model is developed according to historical data logged in the field and then validated. Later, the SARIMA model was tested on a real location in the village of Noja, Spain. The analysis of the field test results prove that the proposed model is feasible to be used since there is no significance difference between the target values set in the critical point and the real values measured in the field. The research proves that the SARIMA model can be used as an alternative for critical point control in water distribution networks when no flow signal is available or when communication between the critical point and the pressure reducing valve is not an option. [ABSTRACT FROM AUTHOR]

From a public health perspective, tobacco use is addictive by nature and triggers several cancers, cardiovascular and respiratory diseases, reproductive disorders, and many other adverse health effects leading to many deaths. In this context, the need to eradicate tobacco-related health problems and the increasingly complex environments of tobacco research require sophisticated analytical methods to handle large amounts of data and perform highly specialized tasks. In this study, time series models are used: autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to forecast the impact of COVID-19 on sales of cigarette in Spanish provinces. To find the optimal solution, initial combinations of model parameters automatically selected the ARIMA model, followed by finding the optimized model parameters based on the best fit between the predictions and the test data. The analytical tools Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. The evaluation metrics that are used as criteria to select the best model are: mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), mean percentage error (MPE), mean error (ME) and mean absolute standardized error (MASE). The results show that the national average impact is slight. However, in border provinces with France or with a high influx of tourists, a strong impact of COVID-19 on tobacco sales has been observed. In addition, the least impact has been observed in border provinces with Gibraltar. Policymakers need to make the right decisions about the tobacco price differentials that are observed between neighboring European countries when there is constant and abundant cross-border human transit. To keep smoking under control, all countries must make harmonized decisions. [ABSTRACT FROM AUTHOR]

Background: In line with WHO guidelines for the implementation of public health prevention plans targeted at the impacts of high temperatures, a heat wave definition temperature (Tthreshold) was calculated for 182 so-called "isoclimatic zones" (IZ) in Spain. As the dependent variable for determining this Tthreshold, we analysed daily all-cause mortality data (ICD-10: A00-R99) for each IZ across the period 2009–2018. The independent variable used was the mean value of the maximum daily temperature of the summer months recorded at meteorological observatories in each IZ. We used Box–Jenkins models to ascertain mortality anomalies, and scatterplots to link these anomalies to the temperatures at which they occurred, thereby determining the Tthreshold for each IZ. We then calculated how many heat waves had occurred in each IZ, as well as their intensity, and analysed their time trend over this period. Results: The results showed that in 52.5% of the IZ, the percentile of the maximum temperatures series of the summer months to which Tthreshold corresponded was below the 95th percentile of the meteorological heat wave definition in Spain: indeed, it only coincided in 30.7% of cases. The geographical distribution of these percentiles displayed great heterogeneity as a consequence of the local factors that influence the temperature–mortality relationship. The trend in the number of heat waves analysed indicated an overall increase in Spain at a rate of 3.9 heat waves per decade, and a similar rise in mean annual intensity of 9.5 °C/decade. These time-trend values were higher than those yielded by analysing the trend in meteorological heat waves based on the 95th percentile. Conclusions: The results obtained in this study indicate the need to use a heat wave definition based on epidemiological temperature–mortality studies, rather than on values based on meteorological percentiles. This could be minimising estimated health impacts in analyses of future impacts attributable to heat. [ABSTRACT FROM AUTHOR]

Time series of mean monthly temperature and total monthly precipitation are two of the climatic variables most easily obtained from weather station records. There are many studies analyzing historical series of these variables, particularly in the Spanish territory. In this study, the series of these two variables in 47 stations of the provincial capitals of mainland Spain were analyzed. The series cover time periods from the 1940s to 2013; the studies reviewed in mainland Spain go up to 2008. ARIMA models were used to represent their variation. In the preliminary phase of description and identification of the model, a study to detect possible trends in the series was carried out in an isolated manner. Significant trends were found in 15 of the temperature series, and there were trends in precipitation in only five of them. The results obtained for the trends are discussed with reference to those of other, more detailed studies in the different regions, confirming whether the same trend was maintained over time. With the ARIMA models obtained, 12-month predictions were made by measuring errors with the observed data. More than 50% of the series of both were modeled. Predictions with these models could be useful in different aspects of seasonal job planning, such as wildfires, pests and diseases, and agricultural crops. [ABSTRACT FROM AUTHOR]